A Statistically Representative Atlas for Mapping Neuronal Circuits in the Drosophila Adult Brain

BIFROST: A method for registering diverse imaging datasets of the Drosophila brain

Imaging methods that span both functional measures in living tissue and anatomical measures in fixed tissue have played critical roles in advancing our understanding of the brain. However, making direct comparisons between different imaging modalities, particularly spanning living and fixed tissue, has remained challenging. For example, comparing brain-wide neural dynamics across experiments and aligning such data to anatomical resources, such as gene expression patterns or connectomes, requires precise alignment to a common set of anatomical coordinates. However, reaching this goal is difficult because registering in vivo functional imaging data to ex vivo reference atlases requires accommodating differences in imaging modality, microscope specification, and sample preparation. We overcome these challenges in Drosophila by building an in vivo reference atlas from multiphoton-imaged brains, called the Functional Drosophila Atlas. We then develop a registration pipeline, BrIdge For Registering Over Statistical Templates (BIFROST), for transforming neural imaging data into this common space and for importing ex vivo resources such as connectomes. Using genetically labeled cell types as ground truth, we demonstrate registration with a precision of less than 10 microns. Overall, BIFROST provides a pipeline for registering functional imaging datasets in the fly, both within and across experiments.

BIFROST: a method for registering diverse imaging datasets of the Drosophila brain

The heterogeneity of brain imaging methods in neuroscience provides rich data that cannot be captured by a single technique, and our interpretations benefit from approaches that enable easy comparison both within and across different data types. For example, comparing brain-wide neural dynamics across experiments and aligning such data to anatomical resources, such as gene expression patterns or connectomes, requires precise alignment to a common set of anatomical coordinates. However, this is challenging because registering in vivo functional imaging data to ex vivo reference atlases requires accommodating differences in imaging modality, microscope specification, and sample preparation. We overcome these challenges in Drosophila by building an in vivo reference atlas from multiphoton-imaged brains, called the Functional Drosophila Atlas (FDA). We then develop a two-step pipeline, BrIdge For Registering Over Statistical Templates (BIFROST), for transforming neural imaging data into this common space and for importing ex vivo resources such as connectomes. Using genetically labeled cell types as ground truth, we demonstrate registration with a precision of less than 10 microns. Overall, BIFROST provides a pipeline for registering functional imaging datasets in the fly, both within and across experiments.

Significance

Large-scale functional imaging experiments in Drosophila have given us new insights into neural activity in various sensory and behavioral contexts. However, precisely registering volumetric images from different studies has proven challenging, limiting quantitative comparisons of data across experiments. Here, we address this limitation by developing BIFROST, a registration pipeline robust to differences across experimental setups and datasets. We benchmark this pipeline by genetically labeling cell types in the fly brain and demonstrate sub-10 micron registration precision, both across specimens and across laboratories. We further demonstrate accurate registration between in-vivo brain volumes and ultrastructural connectomes, enabling direct structure-function comparisons in future experiments.

DLATA: Deep Learning-Assisted transformation alignment of 2D brain slice histology

Accurate alignment of brain slices is crucial for the classification of neuron populations by brain region, and for quantitative analysis in in vitro brain studies. Current semi-automated alignment workflows require labor intensive labeling of feature points on each slice image, which is time-consuming. To speed up the process in large-scale studies, we propose a method called Deep Learning-Assisted Transformation Alignment (DLATA), which uses deep learning to automatically identify feature points in images after training on a few labeled samples. DLATA only requires approximately 10% of the sample size of other semi-automated alignment workflows. Following feature point recognition, local weighted mean method is used as a geometrical transformation to align slice images for registration, achieving better results with about 4 fewer pixels of error than other semi-automated alignment workflows. DLATA can be retrained and successfully applied to the alignment of other biological tissue slices with different stains, including the typically challenging fluorescent stains. Reference codes and trained models for Nissl-stained coronal brain slices of mice can be found at https://github.com/ALIGNMENT2023/DLATA.

Cell-Surface Targeting of Fluorophores in Drosophila for Rapid Neuroanatomy Visualization

Visualizing neuronal anatomy often requires labor-intensive immunohistochemistry on fixed and dissected brains. To facilitate rapid anatomical staining in live brains, we used genetically targeted membrane tethers that covalently link fluorescent dyes for in vivo neuronal labeling. We generated a series of extracellularly trafficked small-molecule tethering proteins, HaloTag-CD4 (Kirk et al. Front. Neurosci. 2021, 15, 754027) and SNAPf-CD4, which directly label transgene-expressing cells with commercially available ligand-substituted fluorescent dyes. We created stable transgenic Drosophila reporter lines, which express extracellular HaloTag-CD4 and SNAPf-CD4 with LexA and Gal4 drivers. Expressing these enzymes in live Drosophila brains, we labeled the expression patterns of various Gal4 driver lines recapitulating histological staining in live-brain tissues. Pan-neural expression of SNAPf-CD4 enabled the registration of live brains to an existing template for anatomical comparisons. We predict that these extracellular platforms will not only become a valuable complement to existing anatomical methods but will also prove useful for future genetic targeting of other small-molecule probes, drugs, and actuators.

Statistical Atlases and Automatic Labeling Strategies to Accelerate the Analysis of Social Insect Brain Evolution

Current methods used to quantify brain size and compartmental scaling relationships in studies of social insect brain evolution involve manual annotations of images from histological samples, confocal microscopy or other sources. This process is susceptible to human bias and error and requires time-consuming effort by expert annotators. Standardized brain atlases, constructed through 3D registration and automatic segmentation, surmount these issues while increasing throughput to robustly sample diverse morphological and behavioral phenotypes. Here we design and evaluate three strategies to construct statistical brain atlases, or templates, using ants as a model taxon. The first technique creates a template by registering multiple brains of the same species. Brain regions are manually annotated on the template, and the labels are transformed back to each individual brain to obtain an automatic annotation, or to any other brain aligned with the template. The second strategy also creates a template from multiple brain images but obtains labels as a consensus from multiple manual annotations of individual brains comprising the template. The third technique is based on a template comprising brains from multiple species and the consensus of their labels. We used volume similarity as a metric to evaluate the automatic segmentation produced by each method against the inter- and intra-individual variability of human expert annotators. We found that automatic and manual methods are equivalent in volume accuracy, making the template technique an extraordinary tool to accelerate data collection and reduce human bias in the study of the evolutionary neurobiology of ants and other insects.

A micro-CT-based standard brain atlas of the bumblebee

In recent years, bumblebees have become a prominent insect model organism for a variety of biological disciplines, particularly to investigate learning behaviors as well as visual performance. Understanding these behaviors and their underlying neurobiological principles requires a clear understanding of brain anatomy. Furthermore, to be able to compare neuronal branching patterns across individuals, a common framework is required, which has led to the development of 3D standard brain atlases in most of the neurobiological insect model species. Yet, no bumblebee 3D standard brain atlas has been generated. Here we present a brain atlas for the buff-tailed bumblebee Bombus terrestris using micro-computed tomography (micro-CT) scans as a source for the raw data sets, rather than traditional confocal microscopy, to produce the first ever micro-CT-based insect brain atlas. We illustrate the advantages of the micro-CT technique, namely, identical native resolution in the three cardinal planes and 3D structure being better preserved. Our Bombus terrestris brain atlas consists of 30 neuropils reconstructed from ten individual worker bees, with micro-CT allowing us to segment neuropils completely intact, including the lamina, which is a tissue structure often damaged when dissecting for immunolabeling. Our brain atlas can serve as a platform to facilitate future neuroscience studies in bumblebees and illustrates the advantages of micro-CT for specific applications in insect neuroanatomy.

Visualizing anatomically registered data with brainrender

Three-dimensional (3D) digital brain atlases and high-throughput brain-wide imaging techniques generate large multidimensional datasets that can be registered to a common reference frame. Generating insights from such datasets depends critically on visualization and interactive data exploration, but this a challenging task. Currently available software is dedicated to single atlases, model species or data types, and generating 3D renderings that merge anatomically registered data from diverse sources requires extensive development and programming skills. Here, we present brainrender: an open-source Python package for interactive visualization of multidimensional datasets registered to brain atlases. Brainrender facilitates the creation of complex renderings with different data types in the same visualization and enables seamless use of different atlas sources. High-quality visualizations can be used interactively and exported as high-resolution figures and animated videos. By facilitating the visualization of anatomically registered data, brainrender should accelerate the analysis, interpretation, and dissemination of brain-wide multidimensional data.

An unbiased template of the Drosophila brain and ventral nerve cord

The fruit fly Drosophila melanogaster is an important model organism for neuroscience with a wide array of genetic tools that enable the mapping of individual neurons and neural subtypes. Brain templates are essential for comparative biological studies because they enable analyzing many individuals in a common reference space. Several central brain templates exist for Drosophila, but every one is either biased, uses sub-optimal tissue preparation, is imaged at low resolution, or does not account for artifacts. No publicly available Drosophila ventral nerve cord template currently exists. In this work, we created high-resolution templates of the Drosophila brain and ventral nerve cord using the best-available technologies for imaging, artifact correction, stitching, and template construction using groupwise registration. We evaluated our central brain template against the four most competitive, publicly available brain templates and demonstrate that ours enables more accurate registration with fewer local deformations in shorter time.

Advances in multimodal data fusion in neuroimaging: Overview, challenges, and novel orientation

Multimodal fusion in neuroimaging combines data from multiple imaging modalities to overcome the fundamental limitations of individual modalities. Neuroimaging fusion can achieve higher temporal and spatial resolution, enhance contrast, correct imaging distortions, and bridge physiological and cognitive information. In this study, we analyzed over 450 references from PubMed, Google Scholar, IEEE, ScienceDirect, Web of Science, and various sources published from 1978 to 2020. We provide a review that encompasses (1) an overview of current challenges in multimodal fusion (2) the current medical applications of fusion for specific neurological diseases, (3) strengths and limitations of available imaging modalities, (4) fundamental fusion rules, (5) fusion quality assessment methods, and (6) the applications of fusion for atlas-based segmentation and quantification. Overall, multimodal fusion shows significant benefits in clinical diagnosis and neuroscience research. Widespread education and further research amongst engineers, researchers and clinicians will benefit the field of multimodal neuroimaging.

The natverse, a versatile toolbox for combining and analysing neuroanatomical data

To analyse neuron data at scale, neuroscientists expend substantial effort reading documentation, installing dependencies and moving between analysis and visualisation environments. To facilitate this, we have developed a suite of interoperable open-source R packages called the natverse. The natverse allows users to read local and remote data, perform popular analyses including visualisation and clustering and graph-theoretic analysis of neuronal branching. Unlike most tools, the natverse enables comparison across many neurons of morphology and connectivity after imaging or co-registration within a common template space. The natverse also enables transformations between different template spaces and imaging modalities. We demonstrate tools that integrate the vast majority of Drosophila neuroanatomical light microscopy and electron microscopy connectomic datasets. The natverse is an easy-to-use environment for neuroscientists to solve complex, large-scale analysis challenges as well as an open platform to create new code and packages to share with the community.

Division of labor and brain evolution in insect societies: Neurobiology of extreme specialization in the turtle ant Cephalotes varians

Strongly polyphenic social insects provide excellent models to examine the neurobiological basis of division of labor. Turtle ants, Cephalotes varians, have distinct minor worker, soldier, and reproductive (gyne/queen) morphologies associated with their behavioral profiles: small-bodied task-generalist minors lack the phragmotic shield-shaped heads of soldiers, which are specialized to block and guard the nest entrance. Gynes found new colonies and during early stages of colony growth overlap behaviorally with soldiers. Here we describe patterns of brain structure and synaptic organization associated with division of labor in C. varians minor workers, soldiers, and gynes. We quantified brain volumes, determined scaling relationships among brain regions, and quantified the density and size of microglomeruli, synaptic complexes in the mushroom body calyxes important to higher-order processing abilities that may underpin behavioral performance. We found that brain volume was significantly larger in gynes; minor workers and soldiers had similar brain sizes. Consistent with their larger behavioral repertoire, minors had disproportionately larger mushroom bodies than soldiers and gynes. Soldiers and gynes had larger optic lobes, which may be important for flight and navigation in gynes, but serve different functions in soldiers. Microglomeruli were larger and less dense in minor workers; soldiers and gynes did not differ. Correspondence in brain structure despite differences in soldiers and gyne behavior may reflect developmental integration, suggesting that neurobiological metrics not only advance our understanding of brain evolution in social insects, but may also help resolve questions of the origin of novel castes.